IJRCM-1_vol-7_2016_issue-04-art-06VOLUME NO. 7 (2016), ISSUE NO. 04
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ii
CONTENTS
TITLE & NAME OF THE AUTHOR (S) Page No.
1. UNDERSTANDING SEASONALITY AND ITS IMPACT ON DEMAND FORECASTING:
THE CASE OF
INDIAN PAINT INDUSTRY
1
2. AN EMPIRICAL INVESTIGATION IN THE ROLE OF MICRO-FINANCE
INSTITUTIONS FOSTERING
FINANCIAL INCLUSION FOR WOMEN IN HARYANA
RAVI DUTT & DR. R. K. SHARMA
6
3. DOMESTIC DEBT, EXTERNAL DEBT AND ECONOMIC GROWTH OF JORDAN
ALI MUSTAFA AL-QUDAH
11
4. EVIDENCE AGAINST EXISTENCE OF SIZE EFFECT IN THE INDIAN
CONTEXT
DR. G. RAGHURAM
17
5. DETERMINANTS OF MOST INFLUENCING REFERENCE GROUP IN BUYING
DECISION OF RURAL
CONSUMER
23
6. DECISION SUPPORT SYSTEM IN SUPPLY CHAIN NETWORKS: A CRITICAL
REVIEW
ARTI KRISHNAWAT & DR. SHANKAR CHAUDHARY
28
7. CONSUMER AWARENESS AND FEEDBACK TOWARDS PURCHASE INTENTION OF
CSR RELATED
PRODUCTS USING SEM
37
8. EFFECTS OF IRREGULAR SHIFT WORK ON THE MARITAL LIFE AND
PARENTING DIFFICULTIES (A
STRUCTURAL EQUATION MODELING)
41
9. MEASURING CORRELATION OF SELECTED DISTRICT CENTRAL CO-OPERATIVE
BANK OF GUJARAT
STATE
47
10. INDIAN A STUDY ON ADVERTISING RECALLING ABILITY OF
CHILDREN
D. RENUKA DEVI & DR. P. KANCHANA DEVI
51
DR. SHILPA BAGDARE
DR. SOPNAMAYEE ACHARYA
DR. ASHA NAGENDRA & DR. SAFIA FAROOQUI
65
14. A STUDY ON EMPLOYEE RETENTION MEASURES AND ITS IMPACT ON
EMPLOYEE MOTIVATION
DR. MAHESHA KEMPEGOWDA & PURUSHOTHAM.C.J 68
15. A STUDY ON JOB SATISFACTION AMONG UNDERGRADUATE FACULTIES WITH
SPECIAL REFERENCE
TO SELF FINANCED INSTITUTIONS, BANGALORE
USHA SHREE.V
73
16. AN EMPIRICAL ANALYSIS ON DEMOGRAPHIC TRANSITION AND SAVINGS IN
INDONESIA
SARI LESTARI ZAINAL RIDHO, NURLINA TARMIZI, BAMBANG B. SOEBYAKTO
& AZWARDI
83
KRISHNA A KAPADIA
86
18. GUEST SATISFACTION IN HOTELS OF KASHMIR: THE PERCEPTION OF
FOREIGN TOURISTS
ANJUM ARA
19. ADVERTISING ETHICS AND SURROGATE ADVERTISING PRACTICES: AN
EMPIRICAL STUDY
MONIKA BANGARI
94
20. IMPACT OF PROMOTIONAL MIX ELEMENTS ON TOURIST’S SATISFACTION: A
CASE STUDY OF
MUSSOORIE
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iii
Chairman, Malaviya National Institute of Technology, Jaipur (An
institute of National Importance & fully funded by Ministry of
Human Resource Development, Government of India)
Chancellor, K. R. Mangalam University, Gurgaon
Chancellor, Lingaya’s University, Faridabad
Founder Vice-Chancellor (1998-2008), Guru Gobind Singh Indraprastha
University, Delhi
Ex. Pro Vice-Chancellor, Guru Jambheshwar University, Hisar
FOUNDER PATRON LATE SH. RAM BHAJAN AGGARWAL
Former State Minister for Home & Tourism, Government of
Haryana
Former Vice-President, Dadri Education Society, Charkhi Dadri
Former President, Chinar Syntex Ltd. (Textile Mills), Bhiwani
FORMER CO-ORDINATOR DR. S. GARG
Faculty, Shree Ram Institute of Business & Management,
Urjani
ADVISORS PROF. M. S. SENAM RAJU
Director A. C. D., School of Management Studies, I.G.N.O.U., New
Delhi
PROF. M. N. SHARMA Chairman, M.B.A., Haryana College of Technology
& Management, Kaithal
PROF. S. L. MAHANDRU Principal (Retd.), Maharaja Agrasen College,
Jagadhri
EDITOR PROF. R. K. SHARMA
Professor, Bharti Vidyapeeth University Institute of Management
& Research, New Delhi
CO-EDITOR DR. BHAVET
EDITORIAL ADVISORY BOARD DR. RAJESH MODI
Faculty, Yanbu Industrial College, Kingdom of Saudi Arabia
PROF. SANJIV MITTAL University School of Management Studies, Guru
Gobind Singh I. P. University, Delhi
PROF. ANIL K. SAINI Chairperson (CRC), Guru Gobind Singh I. P.
University, Delhi
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DR. SAMBHAVNA Faculty, I.I.T.M., Delhi
DR. MOHENDER KUMAR GUPTA Associate Professor, P. J. L. N.
Government College, Faridabad
DR. SHIVAKUMAR DEENE Asst. Professor, Dept. of Commerce, School of
Business Studies, Central University of Karnataka, Gulbarga
ASSOCIATE EDITORS PROF. NAWAB ALI KHAN
Department of Commerce, Aligarh Muslim University, Aligarh,
U.P.
PROF. ABHAY BANSAL Head, Department of I.T., Amity School of
Engineering & Technology, Amity University, Noida
PROF. V. SELVAM SSL, VIT University, Vellore
PROF. N. SUNDARAM VIT University, Vellore
DR. PARDEEP AHLAWAT Associate Professor, Institute of Management
Studies & Research, Maharshi Dayanand University, Rohtak
DR. S. TABASSUM SULTANA Associate Professor, Department of Business
Management, Matrusri Institute of P.G. Studies, Hyderabad
DR. JASVEEN KAUR Asst. Professor, University Business School, Guru
Nanak Dev University, Amritsar
FORMER TECHNICAL ADVISOR AMITA
FINANCIAL ADVISORS DICKIN GOYAL
Advocate & Tax Adviser, Panchkula
LEGAL ADVISORS JITENDER S. CHAHAL
Advocate, Punjab & Haryana High Court, Chandigarh U.T.
CHANDER BHUSHAN SHARMA Advocate & Consultant, District Courts,
Yamunanagar at Jagadhri
SUPERINTENDENT SURENDER KUMAR POONIA
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• Bowersox, Donald J., Closs, David J., (1996), "Logistical
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• Hunker, H.L. and A.J. Wright (1963), "Factors of Industrial
Location in Ohio" Ohio State University, Nigeria.
CONTRIBUTIONS TO BOOKS
• Sharma T., Kwatra, G. (2008) Effectiveness of Social Advertising:
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Edited by
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to Corporate Social Responsibility, Chapter 15, pp 287-303.
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• Garg, Sambhav (2011): "Business Ethics" Paper presented at the
Annual International Conference for the All India Management
Association,
New Delhi, India, 19–23
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• Kumar S. (2011): "Customer Value: A Comparative Study of Rural
and Urban Customers," Thesis, Kurukshetra University,
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• Garg, Bhavet (2011): Towards a New Gas Policy, Political Weekly,
Viewed on January 01, 2012
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DECISION SUPPORT SYSTEM IN SUPPLY CHAIN NETWORKS: A CRITICAL
REVIEW
ARTI KRISHNAWAT
RESEARCH SCHOLAR
UDAIPUR
UDAIPUR
ABSTRACT This article is a review of work published in various
journals/books/conference proceedings/news on the topics of
Decision Support System and Supply Chain Net-
works between June 2003 and March 2015. A total of 73 articles from
49 journals/books are reviewed. The article intends to serve three
goals. First the article will
help researchers in understanding various questions addressed,
models proposed in the area of DSS and SCN with major emphasis on
agriculture and food pro-
cessing industry. Second, the article will be a useful resource for
searching for research topics related to DSS in SCN of agriculture
and food processing industry.
Third, it will serve as a comprehensive bibliography of the
articles published during the period. The literature is analyzed
under 4 major themes and nine sub-themes.
KEYWORDS Decision Support System (DSS), Supply Chain Network (SCN),
system, model, agriculture, Food Processing Industries (FPI).
1. INTRODUCTION ecision support systems (DSS) is the natural
framework where decision models should be included in order to
support farmers, advisers or livestock
management specialists, supply chain management and operational
team in the effective decision making process. During last years,
the increment of
competition between food product producers caused the marginal
benefits per unit of product to reduce. In this context, there is
an increasing interest in
DSS tools capable of dealing with the uncertainty inherent to food
production systems for practical decision support. In this paper
various DSS models in supply
chain networks, the development of DSS in agricultural or other
industries representing either the productive and transportations
over time and their mathematical
foundation are reviewed. DSS in SCN is an industry-driven concept
and system and is universally accepted by manufacturing industry as
well as food processing
industry (to a certain extent) as a practical solution to achieve
integrated information system. The academic research community has
been contributing to the
field in various ways. A typical way of contributing to a field is
by publishing archival journal papers for public benefits. This
article is a review of work published in
various journals/books/conference proceedings/news on the topics of
Decision Support System and Supply Chain Networks between June 2003
and March 2015.
A total of 73 articles from 49 journals/books are reviewed. No
restrictions are imposed on the field of the
journals/book/conference proceedings/reports/websites
thus representing truly multi-disciplinary views on DSS in
SCN.
As national and international concern over sustainable resources
becomes more prevalent, the need for decision support systems (DSS)
increases. The article will
enable the researchers in understanding the applicable uses of
successful DSS in SCN models proposed or designed in field of
agriculture and allied industries as
well as other industries.
The article is divided into four remaining sections. Section 2
describes the methodology followed in collecting and analyzing the
articles. Section 3 provides the
aggregate properties of these articles for each major theme. Some
analyses of statistics on the reviewed articles along with a few
obvious trends are provided in
Section 4. The paper concludes with Section 5.
2. METHODOLOGY The criteria for choosing articles/book
section/reports for the review are as follows. First of all, the
article must have been published in a peer-review, archival
journal. The conference proceedings, book section and reports
should also be from well-established publication. Second, only the
articles and publications with
'DSS in Agriculture' and 'DSS in SCN' as a part of their titles
were selected. The exceptions are those articles that are
explicitly dealing with ‘DSS in food processing
industry’ or 'DSS in SCN in agriculture' but for some reasons the
authors decided not to use ‘DSS’ or 'SCN' in the title. The
inclusion of such articles is inevitably ad
hoc. Consequently, it is possible that there exist more of such
articles which are not surveyed in this article. In an attempt to
avoid never ending revision of the
article, March 2015 was selected as the cut-off date. According to
these criteria, an effort has been made to collect and compile all
the available journal articles
and publications through exhaustive internet browsing, database
search, reference checking, etc. However, it is always possible
that some of the articles are
mislaid from this list. The complete list of the journals, books,
conference proceedings and reports along with the number of
articles appeared respectively is
presented in Table 1,2,3 and 4 respectively.
D
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TABLE 1: NUMBER OF ARTICLES IN EACH JOURNAL /BOOK
SECTION/CONFERENCE PROCEEDINGS/REPORTS/ WEBSITES (ALL IN
ALPHABETICAL ORDER)
Name of Journal Number of articles
Agrociencia 1
Decision Support Systems 4
European Journal of Operational Research 3
Expert Systems with Applications 1
Food and Agriculture Organization of the United Nations 1
Industrial Journal of Productivity and Performance Management
1
Industrial Management and Data System 2
Information Systems & Supply Chain Management 1
Innovations in Agri-Food Systems 1
International Journal of Computer Science Issues 1
International Journal of Engineering and Science 1
International Journal of Information Systems and Supply Chain
Management 1
International Journal of Logistics and Supply Chain Management
Perspectives 1
International Journal of Logistics Research and Applications
2
International Journal of Physical Distribution and Logistics
Management 2
International Journal of Production Research 4
International Journal on Food System Dynamics 1
Issues in Information Systems 1
Journal of Business and Industrial Marketing 2
Journal of Enterprise Information Management 1
Journal of Food Engineering 1
Journal of Manufacturing Technology Management 1
Journal of Marketing Channels 1
Journal of Purchasing and Supply Management 1
Omega 1
Supply Chain Management :An International Journal 1
TEKNOLOGI 1
Total 53
TABLE 2: NUMBER OF BOOKS /BOOK SECTION ON DSS IN SCN (All in
alphabetical order)
Name of Book Number of Chapters
Agro-Industrial Supply Chain Management: Concepts and Applications
1
Decision Support Systems in Agriculture, Food and the Environment:
Trends, Applications and Advances 3
Encyclopedia of Decision Making and Decision Support Technologies
1
Handbook on Decision Making 1
Total 6
TABLE 3: NUMBER OF CONFERENCE PROCEEDINGS ON DSS IN SCN (All in
alphabetical order)
Name of Conference Number of Publications
14th Euroma 2007 Conference 1
3rd Annual IEEE Conference on Automation Science and Engineering
1
8th International Symposium on Process Systems Engineering 1
AFITA 2010 International Conference 1
ESCIE-The Socializing model for online learning 1
IEEE Conference of Industrial Electronics and Applications 1
IEEE International Conference on Systems,Man and Cybernetics
1
International Hop Growers' Convention 1
Procedia Computer Science 1
The Quality Information for Competitive Agricultural Based
Production System and Commerce 1
Total 12
Name of Report/Website Number of Publications
Arkansas Business article 1
Vista Foods Report 1
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TABLE 5: MAJOR THEMES AND SUB-THEMES WITHIN DOMAIN OF DSS IN
SCN
Themes
3. Supply Chain Management
4. DSS in Supply Chain Network
Structural Design/Framework in a particular sector
Tools and Tactics
General study
The major themes defined in this article are (1) Decision Support
System, (2) IT intervention in food industry, (3) Supply Chain
Management, (4) DSS in Supply
Chain Network. Under (1) Decision Support System theme, following
three sub-themes are defined: (a) models, (b) case study, (c)
general. For (2) IT intervention
in food industry, there is no sub theme. For (3) Supply Chain
Management theme further three sub-themes are there namely: (a) In
agriculture sector, (b) In allied
industries and (c) basic outline in various sector. For (4) DSS in
supply chain network, we have three sub-theme: (a) Structural
design/framework in a particular
sector, (b) tools and tactics and (c) general study. Table 5 shows
these themes and sub-themes used in this article.
A comprehensive table comprising of these themes and their
classified references for each theme is provided in Table 6.The
references are in APA style following
alphabetical order. It is unavoidable to have an article that is
relevant to more than one theme. For example, an article may
address supply chain management
issue in agriculture sector but also provide general study on DSS
in SCN. In such a case, more weighted theme is chosen to classify
the article according to the
author’s judgment.
TABLE 6: MAJOR THEMES AND SUB-THEMES WITHIN DOMAIN OF DSS AND
SUPPLY CHAIN NETWORK
Themes References
Decision Support System
Model (Agrahari and Tripathi, 2012) (Burhanuddin, Ahmad and Desai,
2007) (Noori and Salimi, 2005) (Okongwu et al., 2012) _(Seema,
Kaur
and Kumar, 2014)_(Suroso and Ramadhan, 2012)_(Widodo et al.,
2006)
Case Study (Adam, Csaki, Prier and Bufacchi, 2012) (Aragones, 2010)
(Lam and Dai, 2012) (Laurensona, Buwaldab, and Walkerc, 2010)
(Pavlovic
et al., 2008) (Stiakakis and Sifaleras, 2010) (Xie, Allen and Ali,
2014)
General (Arason et al., 2010) (Bakhrankova, 2010) (Demirtas and
Ustun, 2008) (Manos, Matsatsinis, Paparrizos and Papathanasiou,
2010)
(Pavlovic and Koumbouli, 2009)
(ICT adoption and e-business activity in 2006, 2006)_(Jayaraman,
Ross and Agarwal, 2008)_(Liang, 2013)_ (Van der Vorst, Beulens
and
Van Beek, 2005)_(Wicki and Dabrowska, 2013)
Supply Chain Management
In Agriculture Sector (Ahumada and Villalobos, 2009) (Apaiah and
Hendrix, 2005) (Bryceson, 2005) (Dabbene, Gay and Socco, 2008)
((Van der vorst, Silva,
& Trienekens, 2007) (Keizer et al., 2014) (Lembito, Seminar,
Kusnadi and Arkeman, 2012) (Sutopo, Hisjam and Yuniaristanto,
2012)
(Taylor, 2005)
In Allied Industries (Perez, Castro, Simons and Gimenez, 2010)
(Silvija and Soric, 2010) (Singh, 2009) (Soysal, Bloemhof and Van
der Vorst, 2012) (Traub,
2012)
ous sectors
(Dreyer et al., 2009) (Giannakis and Louis, 2011) (Ngai, Cheng and
Ho, 2004) (Verdouw, Beulens, Trienekens and Van der Vorst,
2011)
(Wu and O'grady, 2005)
Structural De-
particular sector
(Beheshti, 2010) (Blackhurst, Wu and O'grady, 2007) (Costantino et
al., 2009) (Dotoli et al., 2003)_(Felice and Petrillo, 2013)
(Ferrell,
Rogers, Ferrell and Sawayda, 2013) (Hernandez et al., 2014)
(Kristiantoa, Gunasekaranb, Heloa and Sandhu, 2012) (Kumar and
Viswa-
nadham, 2007) (Kumar et al., 2011) (Lam, Choy and Chung, 2011) (Liu
et al., 2014) (Liu et al.,2013) (Maheut et al., 2014) (Manuj
and
Sahin, 2011) (Marimin et al., 2011) _(Marimin, Djatna, Suharjito,
Nugeraha and Bahar, 2010) _(Ngai et.al., 2012) (Ngai, Peng,
Alexan-
der and Moon, 2014)
Tools and Tactics (Jakhar and Barua, 2014)_(Koh et al., 2013)_(Van
der Vorst, Tromp and Van der Zee, 2009)
General Study (Fink, Jurgen, & Stefan, 2005)_(Koh et al.,
2013)_ (Vorst et al, 2007)
3. OVERVIEW OF THE ARTICLES In this section, a brief summative of
the articles for each theme is provided. It is not intended to
provide detail description of each article. Rather, an attempt
to
draw a collective summary is made in this section. For the articles
reviewed for each theme, refer to Table 6 above.
3.1 Decision Support System
Decision Support System (DSS) is an interactive, flexible, and
adaptable computer based information system that utilizes decision
rules, models, and model base
coupled with a comprehensive database and the decision makers own
insights, leading to specific, implementable decisions in solving
problems that would not be
amenable to management science models. Making the decision could be
defined as integration of result produced at stages of process with
computer, human
logic and integration of previous developed model. Thus, a DSS
supports complex decision making and increases its effectiveness
(Tripathi, 2011).
A group of articles are classified under a sub theme of 'Model'.
These articles typically utilize analytical methods, such as
decision analysis, optimization algorithms,
program scheduling routines, and so on, for developing models to
help decision makers formulate alternatives, analyze their impacts,
and interpret and select
appropriate options for implementation (Adelman, 1992).
Most of the articles have used real dataset to test and compare the
results. Techniques namely Decision Making Grid (DMG),
cross-functional multi-criteria deci-
sion-making, customer-relationship management (CRM) and
knowledge-driven marketing, Policy Analysis Matrix (PAM) are used
to propose new DSS. Few models
proposed by researchers under this sub theme are as follows:
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FIG. 1: THE PROPOSED FRAMEWORK OF DSS BUILDING DATA DRIVEN DSS FOR
AGRICULTURE
Source: Agrahari and Tripathi, 2012
FIG. 2: DSSOPP BUSINESS USE-CASE DIAGRAM FIG. 3: DSSOPP
ARCHITECTURE
Source: Suroso and Ramadhan, 2012
Major papers under sub theme 'Case Study' emphasized on
computerized decision support system designed to provide a standard
framework for the encapsulation
of respective science into decision support “tools” for ultimate
clientele. These decision support tools will thus provide
information which will help decision makers
with specific management decisions.
Finally, group of articles under sub -theme 'General' address
agriculture sector focusing on the exposition of innovative
methodologies, from web-mobile systems
to artificial intelligence and knowledge-based DSS, as well as
their applications in every aspect from harvest planning to
international food production and land
management. They aim to monitoring all functions of an agricultural
process and facilitating decision making by proposing scenarios
towards satisfying specific
performance criteria and restrictions. Their aim is to contribute
in research by bridging the gap between theory and practice.
3.2 IT intervention in Food Industry
Information is playing an increasing role in today's economy. It is
treated as a more valuable resource than traditional material
goods. The management of the
flow of goods and services and their accompanying information is
the subject of logistics. Current logistics systems in various
sectors of the economy, especially
that of production, are supported by dedicated integrated IT
systems.
Most papers under this theme deals with identifying problems that
companies face when they handle product returns along the channels
and present the critical
role that information technology and collaboration can play to
mitigate many of the problems and deficiencies. These papers
attempt to highlight a key element
in reducing uncertainties in the different stages of the reverse
channel of a supply chain which is access to accurate and timely
information on the status, location,
and condition of products moving about in the supply chain. Their
analysis is based on literature, interviews, case studies and
survey among decision-makers in
respective enterprises.
3.3 Supply Chain Management
The supply chain of agricultural products has received a great deal
of attention lately due to issues related to public health.
Something that has become apparent
is that in the near future the design and operation of agricultural
supply chains will be subject to more stringent regulations and
closer monitoring, in particular
those for products destined for human consumption (agri-foods).
Agribusiness firms are responding to the emerging challenges in
global economy by seeking the
benefit of greater collaboration and integration with both their
suppliers and customers to ensure more sustainable and profitable
trading arrangements
A group of articles are classified under sub-theme of 'In
Agriculture sector'. These articles typically investigate main
contributions in the field of production and
distribution planning for agri-foods based on agricultural crops.
The articles belonging to this sub-theme tend to focus on
individual cases such as VAG (a prototype
animated, interactive, three-dimensional virtual environment model
of a supply chain in the agribusiness sector), lean value chain
improvement techniques and
Supply-Chain Operations Reference (SCOR model to assist firms in
increasing the effectiveness of their supply chains, and to provide
a process-based approach to
SCM) model to name a few.
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FIG. 4: SUPPLY-CHAIN OPERATIONS REFERENCE (SCOR) MODEL WITH
METRICS
Source: SCOR version 10, Supply Chain Council, 2010., Lembito et.
al., 2012
Some generalisations are occasionally provided in these articles
but emphasis is majorly on those models which have been
successfully implemented.
Articles under sub-theme 'In Allied Industries' particularly
address the supply chain models for agricultural fresh products
such as catalian pork, olive oil as well as
retail industry. Supply chain management (SCM) models of
agricultural fresh products are more complicated than the SCM
models of the usual industrial products
with no deterioration, because the amount of harvestable fresh
products depends on the growing process of the related plants on
farmland, and because the
deteriorating process of the fresh products starts immediately
after harvested (Widodo, Nagasawa, Morizawa, Ota, 2006). What
differentiates agri-food supply
chains from other supply chains is the importance played by factors
such as food quality and safety, and weather related variability
(Salin, 1998). Because of all
these issues, in the context of agri food supply chain, under this
sub-theme, three main functional areas: harvesting, storage and
production, are taken into
consideration. Case research methodology is generally used in the
articles with a conceptual model proposal as a tool to carry out
the assessment.
Some articles attempt to understand the direction of various
industries towards their respective supply chains. Under sub-theme
'General study of various sectors'
articles mainly proposes framework of decision support system for
the management disruptions and mitigation of risks in manufacturing
supply chains. The oper-
ation of global supply chains is challenging due to the complexity
in product and information flows, diversity in sites, localization
and processes and the information
processing needed for coordination and control. Most of the
articles under this sub-theme presents global control model,
performance measurement system,
information and communications technology (ICT) and organization of
roles and responsibilities. Along with this an article on Trans-Net
[critical success factors
(CSFs) of web-based supply-chain management systems (WSCMS)] is
also covered in this sub-theme.
3.4 DSS in Supply Chain network
The agri-food sector is facing global challenges that can only be
met with support of information technologies (IT), (Schiefer,
2004). IT opportunities are key tools
in the agri-food supply chain activities and contribute to the
optimization and to an efficient decision making process. The
decision support systems involve the
storage and processing of collected information, methods and
techniques giving the new information useful for efficient decision
making and in this way make the
planning process more structural and promising. The most famous
decision support systems are Enterprise Resource Planning (ERP) and
Supply Chain Analytics
(SCA), or Advanced Planning Systems (APS).
Reflecting such a level of importance, the largest number of
articles belongs to this theme 'DSS in Supply Chain Network'. It
comprises more than 40% of the entire
articles. Some articles attempt to explain why decision making in
agri-business sector,in particular, is complex and what needs to be
done to achieve desirable
results. Also, various models of implementation stages and
different implementation methodologies are presented. Several
articles present various types of frame-
work or models of DSS in SCN. They range from a conceptual model
that explains the DSS, to the taxonomy of success factors of DSS
implementation and to a user
acceptance model.
One of the popular topic in DSS in Supply Chain network is '
Structural Design/Framework in a particular sector'. The articles
seek to present a decision support
model for improving supply chain performance. In most of the
articles focus is to develop a comprehensive model of supply chain
and supply chain decision-
making complexity that provides an understanding of the drivers of
supply chain complexity and strategies to manage supply chain and
supply chain decision-
making complexity and outcomes. While there is growing concern with
ethics, corporate social responsibility, and sustainability, the
current state of the field is
fragmented with the majority of articles reviewed focusing on
specific issues rather than a more holistic approach. Major
industries explored for case studies are
manufacturing industry,automotive industry,machine tool industry
and agri-business industry. A set of articles in this sub -theme
proposes a decision-focused
knowledge framework including a multi-layer knowledge model (to
capture the know-why and know-with together with the know-what and
know-how), system
dynamic based computer simulation model, ethical decision making
models and green supply chain management (GSCM) practices. Decision
models have been
developed using Soft System Methodology, Hard System Methodology,
Montecarlo approach, analytic network process, grounded theory
methodology, general-
isable network based methodology (to model supply chain operations
and the uncertainty of system attributes such as lead time and
cost) and artificial intelligence
system shells VisiRule and Flex, to name a few. Most of the
articles have adopted case-based reasoning to support managers and
other ultimate clientele in making
appropriate decision arising in supply chain networks. They have
retrieved similar cases in the past and have adapted in reference
to their new model/framework
and then conducted case study to illustrate the feasibility and
effectiveness of the proposed system. The models proposed in
various articles under this sub-theme
provides the foundation for future research as well as support for
decision making when various decision makers are involved. The
results of the research will help
practitioners better understand the sources and outcomes of supply
chain complexity and how to manage it.
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FIG. 5: CONCEPTUAL FRAMEWORK OF iDSS (Intelligent decision support
system for Agricultural business and industry supply chain
management)
Source : Marimin, Djatna, Suharjito, Nugeraha and Bahar,
2010.
FIG. 6: THE THREE LAYERED STRUCTURE OF DSS (A DECISION SUPPORT
SYSTEM FOR INTEGRATED PRODUCTION NETWORK DESIGN CONSIDERING ALSO
THE E-
BUSINESS RELATIONSHIPS BETWEEN OPERATORS AND THE NETWORK'S
ENVIRONMENTAL IMPACT)
Source: Dotoli et al., 2003
The articles belonging to sub-theme 'Tools and Tactics' address
important performance evaluation criteria (supply chain planning
performance, supply chain part-
nership performance, production performance, delivery and logistic
performance and customer service and satisfaction performance) and
corresponding sub-
criteria. As per articles covered under this sub-theme simulation
tools are often used for supporting decision-making on supply chain
(re)design when logistic
uncertainties are in place, building on their inherent modeling
flexibility. One article in particular has propose a new integrated
approach towards logistics, sus-
tainability and food quality analysis, and implement the approach
by introducing a new simulation environment as quality change is
intrinsic to the food supply
chains industry.
Under sub-theme 'General' authors have attempted to identify
methodological shortcomings in existing tools, and proposing a
supply chain (SC) framework which
provide businesses with a holistic understanding of their supply
chains and ensuring partners within supply chain collaborative
networks. They have discussed
about opportunities to use time-dependent product quality
information to improve the design of food supply chain
networks.Data warehouses and data mining
can be used to store and analyze product, inventory, and sales
information. Simulation and optimization, which can be found in
advanced planning and scheduling
systems, can be employed for e.g. inventory, production,
procurement, and distribution planning. Intelligent agents can e.g.
communicate with different partners
in the supply chain, assist in collecting information, share
product information, negotiate prices, and distribute alerts
throughout the logistics networks. They have
proposed heterogenous yet complimentary ensemble of various real
world decision situations contributing to a minitrack which deals
with intelligent decision
support in whole field of e-logistics and supply chain management.
Research carried out in these articles have implications for future
sustainability research in
supply chain, decisions science, management theory, practice and
policy.
4. ANALYSIS The field of DSS has matured in a relatively short
period of time. As Table 4 shows, the number of journal articles
published from 2003 has steadily increased, but
there is a sign of stabilizing in recent years. Considering the
fact that most of journal articles started appearing in late 1990s,
this field certainly gained significant
research interests from many researchers in a short period of
time.
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TABLE 7: NUMBER OF JOURNAL ARTICLES ON DSS IN SCN DURING 2003–2015
(as of March 2015)
Year Number of publications
2003 1
2004 2
2005 6
2006 2
2007 5
2008 6
2009 7
2010 10
2011 5
2012 14
2013 6
2014 8
2015 1
Total 73
From table 6 it could be inferred that almost 40% articles have
published from year 2010-2012. In early years, more articles were
written to share the experiences
of implementing decision support systems or based on opinion survey
studies. As more experiences have been gained with the
implementation process, different
topics such as the importance of using DSS in supply chain network,
DSS framework in agri-business and food processing industry seem to
be becoming of interests
to the researchers. Also, the mature status of the field is evident
in the rigor and thoroughness of the articles in recent
years.
5. CONCLUSION Several areas for future research seem promising. A
large scale, simultaneous survey studies might generate useful
insights on this subject. Major articles present
the development of DSS for managing agricultural and environmental
systems, focusing on the exposition of innovative methodologies,
from web-mobile systems
to artificial intelligence and knowledge-based DSS, as well as
their applications in every aspect from harvest planning to
international food production and land
management, warehouse management, logistics and supply chain
management. The concept of DSS seems to be growing and expanding.
As agricultural produc-
tion and environmental management involve high risk decisions, risk
analysis and management tools have enjoyed increased popularity
over the last years as well,
by the time that food supply chain management, ecosystem
governance, conservation of biodiversity and global climate change
have among other issues hotly
entered the agenda. It will be useful to investigate topics such as
how the companies (FMCG in particular) using the decision support
system perceive this trends,
how they will cope with the changes, what tools, methodologies,
models are useful in their expansion efforts.
This article provides a comprehensive review of research articles
related to the application of decision support in supply chain
networks of various industries with
major emphasis on agribusiness units. Data were obtained from 73
articles published from 2003 to 2015.A comprehensive list of
journal articles identified in this
study provides insights and relevant references for both
researchers and practitioners on the application of decision
support system to various stages of supply
chain. In light of the developed classification framework, gaps
could be identified for the use of the decision support system in
the industry and suggest potential
and applicable research areas for further consideration in this
subject area.
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